“… denotes the waning rate against immunity, and denotes the vaccination rate. To keep the model simple we have neglected the parameters which involve the birth and death issues [5] , [11] , [15] , [16] , [17] , [22] , [40] , [55] . Fig.…”
Section: Model Depictionmentioning
confidence: 99%
“…Thus, the entire human population can be split into two groups when vaccination becomes voluntary provaccination and antivaccination groups meaning getting vaccinated and not getting vaccinated respectively. The decisions of individuals toward vaccination are influenced by the overall number of infected people in each group and the total payoff of both groups’ illnesses [8] , [9] , [10] , [11] , [12] .…”
Section: Introductionmentioning
confidence: 99%
“…However, in certain instances, the recovered individual relapse, a condition known as the waning of immunity. Exposed (E), quarantine (Q), hospitalized (H), and asymptomatic (A) compartments can be used in some epidemics to adequately examine disease dynamics [2] , [4] , [5] , [11] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] . An additional application of compartmental models in the research of disease transmission is the examination of supervision and moderation methods, such as immunization, and the impact of birthing and passing factors.…”
“… denotes the waning rate against immunity, and denotes the vaccination rate. To keep the model simple we have neglected the parameters which involve the birth and death issues [5] , [11] , [15] , [16] , [17] , [22] , [40] , [55] . Fig.…”
Section: Model Depictionmentioning
confidence: 99%
“…Thus, the entire human population can be split into two groups when vaccination becomes voluntary provaccination and antivaccination groups meaning getting vaccinated and not getting vaccinated respectively. The decisions of individuals toward vaccination are influenced by the overall number of infected people in each group and the total payoff of both groups’ illnesses [8] , [9] , [10] , [11] , [12] .…”
Section: Introductionmentioning
confidence: 99%
“…However, in certain instances, the recovered individual relapse, a condition known as the waning of immunity. Exposed (E), quarantine (Q), hospitalized (H), and asymptomatic (A) compartments can be used in some epidemics to adequately examine disease dynamics [2] , [4] , [5] , [11] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] . An additional application of compartmental models in the research of disease transmission is the examination of supervision and moderation methods, such as immunization, and the impact of birthing and passing factors.…”
“…The most effective protective measure when an epidemic breaks out is vaccination, which is more efficient at reducing the probability of infection and the proportion of disease transmission. [30,[34][35][36][37][40][41][42][43][44] Zuo et al [35] proposed a new SEIR/V-AI model to study the interaction between vaccination behavior and hip-hop, and the results showed that individual sensitivity to information affects individual vaccination behavior. Jain et al [37] analyzed the effects of fear generated by individuals during disease transmission on information diffusion.…”
Nowadays, with the rapid development of the Internet, a large amount of information often accompanies the rapid transmission of disease outbreaks, and more and more scholars are studying the relationship between information and the disease transmission process on the basis of complex networks. In fact, the disease transmission process is very complex, beside the information of this factor, often will be individual behavioral measures and other factors together, most of the previous research to establish a two-layer network model to consider the impact of information on the transmission process of disease, divided into information and behavior respectively on the transmission of diseases less research, in order to more in-depth analyze disease transmission process and the intrinsic influence mechanism, this paper divides information and behavior into two layers and proposes the establishment of a complex network to study the dynamics co-evolution of information diffusion, vaccination behavior, and disease transmission is proposed considering four influential relationships between adjacent layers in multilayer networks. In the information layer, the diffusion process of negative information is described, and the feedback effects of the local and global vaccination are considered; in the behavioral layer, the individual vaccination behavior is described, and the probability of individual receiving vaccination behavior is influenced by two factors:one is the influence of negative information, and the other is the influence of local and global disease severity; in the disease layer, individual susceptibility is considered to be influenced by vaccination behavior. The state transition equations are derived by using the Micro Markov Chain Approach (MMCA), and disease prevalence thresholds are obtained. It was demonstrated through simulation experiments that the negative information diffusion is less influenced by local vaccination behavior, and is mainly influenced by global vaccination behavior; the vaccination behavior is mainly influenced by local disease conditions, is less influenced by global disease conditions; the disease transmission threshold increases with increasing vaccination rate; the scale of disease transmission increases with increasing negative information diffusion rate and decreases with increasing vaccination rate. Finally, it is found that when individual vaccination behavior considers both the influence of negative information and disease, it can increase the disease transmission threshold and reduce the scale of disease transmission, so we should resist the diffusion of negative information, increase vaccination proportion, and take appropriate protective measures in time.
“…In general, when an individual's perceived risk of infection increases, she will choose a specific vaccination strategy (i.e., to vaccinate or not) with a probability that depends on a risk-benefit analysis [50] , [51] , [52] , [53] . Hence, individuals constantly update their strategies to maximize their interests.…”
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